A Conceptual Model for Context Awareness in Ethical Data Management

TL;DR

This paper introduces a bipartite conceptual model comprising context dimensions and ethical requirements to adapt data management practices to varying ethical contexts, enhancing responsible AI development.

Contribution

It proposes a novel bipartite model with Context Dimensions Tree and Ethical Requirements Tree to systematically incorporate ethical considerations into data management.

Findings

The model helps tailor datasets to specific ethical contexts.

Examples demonstrate practical application of the model.

Provides a framework for ethical data preprocessing.

Abstract

Ethics has become a major concern to the information management community, as both algorithms and data should satisfy ethical rules that guarantee not to generate dishonourable behaviours when they are used. However, these ethical rules may vary according to the situation-the context-in which the application programs must work. In this paper, after reviewing the basic ethical concepts and their possible influence on data management, we propose a bipartite conceptual model, composed of the Context Dimensions Tree (CDT), which describes the possible contexts, and the Ethical Requirements Tree (ERT), representing the ethical rules necessary to tailor and preprocess the datasets that should be fed to Data Analysis and Learning Systems in each possible context. We provide some examples and suggestions on how these conceptual tools can be used.